Triple
T12610955
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sterling Heights Police Department |
E301118
|
entity |
| Predicate | hasTypeOfPolice |
P105319
|
FINISHED |
| Object | municipal police |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: municipal police | Statement: [Sterling Heights Police Department, hasTypeOfPolice, municipal police]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTypeOfPolice Context triple: [Sterling Heights Police Department, hasTypeOfPolice, municipal police]
-
A.
policeCharacter
Indicates that one entity serves as a police officer or law-enforcement figure in relation to another entity.
-
B.
policeDepartmentType
chosen
Indicates the specific organizational category or classification of a police department (e.g., municipal, state, federal).
-
C.
hasPoliceTheme
Indicates that something features police, law enforcement, or policing activities as a central theme or focus.
-
D.
typeOfLawEnforcement
Indicates that one entity is a specific kind or category of law enforcement associated with another entity.
-
E.
officerCategory
Indicates the classification or type of officer role that an individual holds within an organization or system.
- F. None of above.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69d7bdea2ca881908f379526c13b1145 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d9559458dc8190bc4d6e697e99d70e |
completed | April 10, 2026, 7:55 p.m. |
| PD | Predicate disambiguation | batch_69d9541894fc8190a0c3706a414279f0 |
completed | April 10, 2026, 7:48 p.m. |
Created at: April 9, 2026, 5:11 p.m.